{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:29:03Z","timestamp":1780054143273,"version":"3.54.0"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2017A040406023"],"award-info":[{"award-number":["2017A040406023"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s13042-021-01319-3","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T04:02:52Z","timestamp":1618891372000},"page":"2389-2405","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fuzzy measure with regularization for gene selection and cancer prediction"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1246-4617","authenticated-orcid":false,"given":"JinFeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZhenYu","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ShuaiHui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WenZhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhad","family":"Pourpanah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"issue":"3","key":"1319_CR1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.5121\/ijdps.2013.4309","volume":"4","author":"BM Gayathri","year":"2013","unstructured":"Gayathri BM, Sumathi CP, Santhanam T (2013) Breast cancer diagnosis using machine learning algorithms-a survey. Int J Distrib Parallel Syst 4(3):105\u2013112","journal-title":"Int J Distrib Parallel Syst"},{"issue":"2","key":"1319_CR2","first-page":"55","volume":"2","author":"S Kharya","year":"2012","unstructured":"Kharya S (2012) Using data mining techniques for diagnosis and prognosis of cancer disease. Int J ComputSciEngInfTechnol 2(2):55\u201366","journal-title":"Int J ComputSciEngInfTechnol"},{"key":"1319_CR3","first-page":"8","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. ComputStructBiotechnol J 13:8\u201317","journal-title":"ComputStructBiotechnol J"},{"issue":"11","key":"1319_CR4","first-page":"362","volume":"3","author":"SS Shajahaan","year":"2013","unstructured":"Shajahaan SS, Shanthi S, Manochitra V (2013) Application of data mining techniques to model breast cancer data. Int J EmergTechnolAdvEng 3(11):362\u2013369","journal-title":"Int J EmergTechnolAdvEng"},{"issue":"4","key":"1319_CR5","first-page":"256","volume":"3","author":"SS Shrivastava","year":"2013","unstructured":"Shrivastava SS, Sant A, Aharwal RP (2013) An overview on data mining approach on breast cancer data. Int J AdvComput Res 3(4):256\u2013262","journal-title":"Int J AdvComput Res"},{"key":"1319_CR6","doi-asserted-by":"publisher","first-page":"7270","DOI":"10.1016\/j.eswa.2012.01.096","volume":"39","author":"CJ Alonso-Gonz\u00e1lez","year":"2012","unstructured":"Alonso-Gonz\u00e1lez CJ, Moro-Sancho QI, Simon-Hurtado A, Varela-Arrabal R (2012) Microarray gene expression classification with few genes: criteria to com- bine attribute selection and classification methods. Expert SystAppl 39:7270\u20137280","journal-title":"Expert SystAppl"},{"key":"1319_CR7","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1016\/j.compbiomed.2013.04.018","volume":"43","author":"Y Cui","year":"2013","unstructured":"Cui Y, Zheng CH, Yang J, Sha W (2013) Sparse maximum margin discriminant analysis for feature extraction and gene selection on gene expression data. ComputBiol Med 43:933\u2013941","journal-title":"ComputBiol Med"},{"key":"1319_CR8","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.bbe.2013.09.007","volume":"34","author":"J Kalina","year":"2014","unstructured":"Kalina J (2014) Classification methods for high-dimensional genetic data. Biocybern Biomed Eng 34:10\u201318","journal-title":"Biocybern Biomed Eng"},{"key":"1319_CR9","doi-asserted-by":"publisher","first-page":"3306","DOI":"10.1093\/bioinformatics\/bts602","volume":"28","author":"Y Piao","year":"2012","unstructured":"Piao Y, Piao M, Park K, Ryu KH (2012) An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data. Bioinformatics 28:3306\u20133315","journal-title":"Bioinformatics"},{"issue":"5439","key":"1319_CR10","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531\u2013537","journal-title":"Science"},{"issue":"19","key":"1319_CR11","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1093\/bioinformatics\/btl386","volume":"22","author":"GC Cawley","year":"2006","unstructured":"Cawley GC, Talbot NLC (2006) Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics 22(19):2348\u20132355","journal-title":"Bioinformatics"},{"key":"1319_CR12","doi-asserted-by":"publisher","first-page":"39832","DOI":"10.1038\/srep39832","volume":"7","author":"LT Zhou","year":"2017","unstructured":"Zhou LT, Cao YH, Lv LL et al (2017) Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study. Sci Rep 7:39832","journal-title":"Sci Rep"},{"key":"1319_CR13","doi-asserted-by":"publisher","first-page":"34759","DOI":"10.1038\/srep34759","volume":"6","author":"G Zhao","year":"2016","unstructured":"Zhao G, Wu Y (2016) Feature subset selection for cancer classification using weight local modularity. Sci Rep 6:34759. https:\/\/doi.org\/10.1038\/srep34759","journal-title":"Sci Rep"},{"issue":"5","key":"1319_CR14","doi-asserted-by":"publisher","first-page":"e0149675","DOI":"10.1371\/journal.pone.0149675","volume":"11","author":"HH Huang","year":"2016","unstructured":"Huang HH, Liu XY, Liang Y (2016) Feature selection and cancer classification via sparse logistic regression with the hybrid L1\/2+2 regularization. PLoS ONE 11(5):e0149675","journal-title":"PLoS ONE"},{"key":"1319_CR15","doi-asserted-by":"publisher","DOI":"10.17485\/ijst\/2016\/v9i48\/107971","author":"L Jayasurya","year":"2016","unstructured":"Jayasurya L, Krishna Anand S (2016) Feature selection for microarray data using WGCNA based fuzzy forest in map reduce paradigm. Indian J SciTechnol. https:\/\/doi.org\/10.17485\/ijst\/2016\/v9i48\/107971","journal-title":"Indian J SciTechnol"},{"issue":"23","key":"1319_CR16","doi-asserted-by":"publisher","first-page":"9326","DOI":"10.1016\/j.eswa.2015.08.016","volume":"42","author":"ZY Algamal","year":"2015","unstructured":"Algamal ZY, Lee MH (2015) Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert SystAppl 42(23):9326\u20139332","journal-title":"Expert SystAppl"},{"issue":"7","key":"1319_CR17","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1109\/TNNLS.2012.2197412","volume":"23","author":"Z Xu","year":"2012","unstructured":"Xu Z, Chang X, Xu F et al (2012) L1\/2 regularization: a thresholding representation theory and a fast solver. IEEE Trans Neural Netw 23(7):1013\u20131027","journal-title":"IEEE Trans Neural Netw"},{"issue":"6","key":"1319_CR18","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.gpb.2017.08.002","volume":"15","author":"L Gao","year":"2017","unstructured":"Gao L, Ye M, Lu X et al (2017) Hybrid method based on information gain and support vector machine for gene selection in cancer classification. Genomics Proteomics Bioinform 15(6):389\u2013395","journal-title":"Genomics Proteomics Bioinform"},{"issue":"1","key":"1319_CR19","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1186\/1471-2105-7-228","volume":"7","author":"KJ Yang","year":"2006","unstructured":"Yang KJ, Cai Z, Li J et al (2006) A stable gene selection in microarray data analysis. BMC Bioinform 7(1):228\u2013228","journal-title":"BMC Bioinform"},{"issue":"1","key":"1319_CR20","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1186\/1471-2105-14-198","volume":"14","author":"Y Liang","year":"2013","unstructured":"Liang Y, Liu C, Luan X et al (2013) Sparse logistic regression with a L1\/2 penalty for gene selection in cancer classification. BMC Bioinform 14(1):198\u2013198","journal-title":"BMC Bioinform"},{"key":"1319_CR21","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.neucom.2018.09.084","volume":"323","author":"M Yuan","year":"2019","unstructured":"Yuan M, Yang Z, Ji G et al (2019) Partial maximum correlation information: a new feature selection method for microarray data classification. Neurocomputing 323:231\u2013243. https:\/\/doi.org\/10.1016\/j.neucom.2018.09.084","journal-title":"Neurocomputing"},{"issue":"9","key":"1319_CR22","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1093\/bioinformatics\/btn081","volume":"24","author":"C Li","year":"2008","unstructured":"Li C, Li H (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24(9):1175\u20131182","journal-title":"Bioinformatics"},{"key":"1319_CR23","unstructured":"Smith V, Forte S, Jordan MI, et al (2015) L1-regularized distributed optimization: a communication-efficient primal-dual framework. https:\/\/arxiv.org\/pdf\/1512.04011v1.pdf"},{"key":"1319_CR24","first-page":"1999","volume":"13","author":"GX Yuan","year":"2012","unstructured":"Yuan GX, Ho CH, Lin CJ (2012) An improved GLMNET for L1-regularized logistic regression. J Mach Learn Res 13:1999\u20132030","journal-title":"J Mach Learn Res"},{"issue":"5","key":"1319_CR25","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3390\/genes9050258","volume":"9","author":"Y Sun","year":"2018","unstructured":"Sun Y, Lu C, Li X (2018) The cross-entropy based multi-filter ensemble method for gene selection. Genes 9(5):258","journal-title":"Genes"},{"key":"1319_CR26","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.asoc.2017.09.038","volume":"62","author":"I Jain","year":"2018","unstructured":"Jain I, Jain VK, Jain R (2018) Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification. Appl Soft Comput 62:203\u2013215","journal-title":"Appl Soft Comput"},{"key":"1319_CR27","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1198\/016214506000000735","volume":"101","author":"H Zou","year":"2006","unstructured":"Zou H (2006) The adaptive LASSO and its oracle properties J. Am Stat Assoc (Taylor & Francis) 101:1418\u20131429","journal-title":"Am Stat Assoc (Taylor & Francis)"},{"key":"1319_CR28","first-page":"246","volume":"37","author":"N Meinshausen","year":"2009","unstructured":"Meinshausen N, Yu B (2009) LASSO-type recovery of sparse representations for high-dimensional data. Ann Stat JSTOR 37:246\u2013270","journal-title":"Ann Stat JSTOR"},{"issue":"3","key":"1319_CR29","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/0165-0114(85)90031-4","volume":"16","author":"Z Wang","year":"1985","unstructured":"Wang Z (1985) Asymptotic structural characteristics of fuzzy measure and their applications. Fuzzy Sets Syst 16(3):277\u2013290","journal-title":"Fuzzy Sets Syst"},{"key":"1319_CR30","doi-asserted-by":"publisher","first-page":"2406","DOI":"10.1109\/TFUZZ.2019.2899809","volume":"27","author":"R Chen","year":"2019","unstructured":"Chen R, Guo S, Wang X et al (2019) Fusion of multi-RSMOTE with fuzzy integral to classify bug reports with an imbalanced distribution. IEEE Trans Fuzzy Syst 27:2406\u20132420","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1319_CR31","doi-asserted-by":"publisher","first-page":"3327","DOI":"10.1007\/s13042-019-00960-3","volume":"10","author":"J Zhai","year":"2019","unstructured":"Zhai J, Zhou X, Zhang S et al (2019) Ensemble RBM-based classifier using fuzzy integral for big data classification. Int J Mach Learn Cybern 10:3327\u20133337","journal-title":"Int J Mach Learn Cybern"},{"key":"1319_CR32","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/S0165-0114(02)00499-2","volume":"139","author":"M Grabisch","year":"2003","unstructured":"Grabisch M (2003) The symmetric Sugeno integral. Fuzzy Sets Syst 139:473\u2013490","journal-title":"Fuzzy Sets Syst"},{"key":"1319_CR33","first-page":"819","volume":"2","author":"Z Wang","year":"2003","unstructured":"Wang Z, Guo HF (2003) A new genetic algorithm for nonlinear multiregressions based on generalized Fuzzy integrals. IEEE IntConf Fuzzy Syst 2:819\u2013821","journal-title":"IEEE IntConf Fuzzy Syst"},{"key":"1319_CR34","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/0165-0114(94)90008-6","volume":"64","author":"T Murofushi","year":"1994","unstructured":"Murofushi T, Sugeno M, Machida M (1994) Non monotonic fuzzy measures and the fuzzy integral. Fuzzy Sets Syst 64:73\u201386","journal-title":"Fuzzy Sets Syst"},{"key":"1319_CR35","doi-asserted-by":"crossref","unstructured":"Wang Z (2003) A new genetic algorithm for nonlinear multiregressions based on generalized Choquet integrals. In: Proc. 12th IEEE intern. conf. fuzzy systems, vol 2, pp 819\u2013821","DOI":"10.1109\/FUZZ.2003.1206535"},{"key":"1319_CR36","first-page":"171","volume":"6","author":"W Wang","year":"1998","unstructured":"Wang W, Wang ZY, Klir GJ (1998) Genetic algorithm for determining fuzzy measures from data. J Intell Fuzzy Syst 6:171\u2013183","journal-title":"J Intell Fuzzy Syst"},{"issue":"5","key":"1319_CR37","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TSMCB.2002.1033182","volume":"32","author":"KS Leung","year":"2002","unstructured":"Leung KS, Wong ML, Lam W, Wang Z, Xu K (2002) Learning nonlinear multiregression networks based on evolutionary computation. IEEE Trans Syst Man Cybern Part B 32(5):630\u2013644","journal-title":"IEEE Trans Syst Man Cybern Part B"},{"key":"1319_CR38","unstructured":"Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Caki F (eds) Second international symposium on information theory, Budapest, pp 267\u2013281"},{"key":"1319_CR39","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461\u2013464","journal-title":"Ann Stat"},{"key":"1319_CR40","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the LASSO. J R Stat Soc B 58:267\u2013288","journal-title":"J R Stat Soc B"},{"key":"1319_CR41","doi-asserted-by":"publisher","first-page":"2845","DOI":"10.1109\/18.959265","volume":"47","author":"DL Donoho","year":"2001","unstructured":"Donoho DL, Huo X (2001) Uncertainty principles and ideal atomic decomposition. IEEE Trans Inf Theory 47:2845\u20132862","journal-title":"IEEE Trans Inf Theory"},{"key":"1319_CR42","doi-asserted-by":"publisher","first-page":"2197","DOI":"10.1073\/pnas.0437847100","volume":"100","author":"DL Donoho","year":"2003","unstructured":"Donoho DL, Elad E (2003) Maximal sparsity representation via l1 minimization. ProcNatlAcalSci 100:2197\u20132202","journal-title":"ProcNatlAcalSci"},{"key":"1319_CR43","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1137\/S003614450037906X","volume":"43","author":"S Chen","year":"2001","unstructured":"Chen S, Donoho DL, Saunders M (2001) Atomic decomposition by basis pursuit. SIAM Rev 43:129\u2013159","journal-title":"SIAM Rev"},{"issue":"6","key":"1319_CR44","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1007\/s11432-010-0090-0","volume":"53","author":"ZB Xu","year":"2010","unstructured":"Xu ZB, Hai Z, Yao W et al (2010) L1\/2 regularization. Sci China InfSci 53(6):1159\u20131169","journal-title":"Sci China InfSci"},{"issue":"1","key":"1319_CR45","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/nm0102-68","volume":"8","author":"MA Shipp","year":"2002","unstructured":"Shipp MA, Ross KN, Tamayo P et al (2002) Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8(1):68\u201374","journal-title":"Nat Med"},{"issue":"2","key":"1319_CR46","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/S1535-6108(02)00030-2","volume":"1","author":"D Singh","year":"2002","unstructured":"Singh D, Febbo PG, Ross K et al (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2):203\u2013209","journal-title":"Cancer Cell"},{"issue":"1","key":"1319_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/cpa.20303","volume":"63","author":"I Daubechies","year":"2010","unstructured":"Daubechies I, Devore R, Fornasier M (2010) Iteratively reweighted least squares minimization for sparse recovery. Commun Pure Appl Math 63(1):1\u201338","journal-title":"Commun Pure Appl Math"},{"issue":"12","key":"1319_CR48","doi-asserted-by":"publisher","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","volume":"96","author":"U Alon","year":"1999","unstructured":"Alon U, Barkai N, Notterman DA et al (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. ProcNatlAcadSci USA 96(12):6745\u20136750","journal-title":"ProcNatlAcadSci USA"},{"issue":"18","key":"1319_CR49","doi-asserted-by":"publisher","first-page":"6503","DOI":"10.1158\/0008-5472.CAN-04-0452","volume":"64","author":"WA Freije","year":"2004","unstructured":"Freije WA, Edmundo Castro-Vargas F, Fang Z et al (2004) Gene expression profiling of gliomas strongly predicts survival. Can Res 64(18):6503\u20136510","journal-title":"Can Res"},{"key":"1319_CR50","volume-title":"Microarray suite user's guide version 5.0","author":"Affymetrix","year":"2001","unstructured":"Affymetrix (2001) Microarray suite user\u2019s guide version 5.0. Affymetrix Inc., Santa Clara"},{"issue":"2","key":"1319_CR51","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","volume":"19","author":"BM Bolstad","year":"2003","unstructured":"Bolstad BM, Irizarry RA, Astrand M et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185\u2013193","journal-title":"Bioinformatics"},{"issue":"468","key":"1319_CR52","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1198\/016214504000000683","volume":"99","author":"Z Wu","year":"2004","unstructured":"Wu Z, Irizarry RA, Gentleman R et al (2004) A model based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc 99(468):909\u2013917","journal-title":"J Am Stat Assoc"},{"key":"1319_CR53","unstructured":"Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the fourteenth international joint conference on artificial intelligence, Montreal, QC, Canada, pp 1137\u20131143"},{"key":"1319_CR54","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.3390\/cancers6021098","volume":"6","author":"Y Lin","year":"2014","unstructured":"Lin Y, Sharma S, John MS (2014) CCL21 Cancer immunotherapy. Cancers 6:1098\u20131110","journal-title":"Cancers"},{"issue":"2","key":"1319_CR55","doi-asserted-by":"publisher","first-page":"e2603","DOI":"10.1038\/cddis.2016.352","volume":"8","author":"K Qu","year":"2017","unstructured":"Qu K, Wang Z, Fan H et al (2017) MCM7 promotes cancer progression through cyclin D1-dependent signaling and serves as a prognostic marker for patients with hepatocellular carcinoma. Cell Death Dis 8(2):e2603. https:\/\/doi.org\/10.1038\/cddis.2016.352","journal-title":"Cell Death Dis"},{"key":"1319_CR56","doi-asserted-by":"publisher","first-page":"14687","DOI":"10.1038\/ncomms14687","volume":"8","author":"R Hill","year":"2017","unstructured":"Hill R, Madureira PA, Ferreira B et al (2017) TRIB2 confers resistance to anti-cancer therapy by activating the serine\/threonine protein kinase AKT. Nat Commun 8:14687","journal-title":"Nat Commun"},{"issue":"3","key":"1319_CR57","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/j.bbrc.2006.06.175","volume":"347","author":"M Kohno","year":"2006","unstructured":"Kohno M, Hasegawa H, Inoue A, Muraoka M, Miyazaki T, Oka K, Yasukawa M (2006) Identification of\u00a0N-arachidonylglycine as the endogenous ligand for orphan G-protein-coupled receptor GPR18. BiochemBiophys Res Commun 347(3):827\u2013832","journal-title":"BiochemBiophys Res Commun"},{"key":"1319_CR58","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.1835","author":"DB Finlay","year":"2016","unstructured":"Finlay DB, Joseph WR, Grimsey NL, Glass M (2016) GPR18 undergoes a high degree of constitutive trafficking but is unresponsive to N-arachidonoyl glycine. PeerJ. https:\/\/doi.org\/10.7717\/peerj.1835","journal-title":"PeerJ"},{"key":"1319_CR59","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.lfs.2018.11.061","volume":"217","author":"L Zhang","year":"2019","unstructured":"Zhang L, Qiu C, Yang L et al (2019) GPR18 expression on PMNs as biomarker for outcome in patient with sepsis. Life Sci 217:49\u201356","journal-title":"Life Sci"},{"key":"1319_CR60","doi-asserted-by":"publisher","DOI":"10.1042\/BSR20181151","author":"WH Ding","year":"2008","unstructured":"Ding WH, Ren KW, Yue C et al (2008) Association between three genetic variants in kallikrein 3 and prostate cancer risk. Biosci Rep. https:\/\/doi.org\/10.1042\/BSR20181151"},{"key":"1319_CR61","doi-asserted-by":"publisher","first-page":"12698","DOI":"10.1021\/acs.analchem.8b02959","volume":"90","author":"J Wang","year":"2018","unstructured":"Wang J, Koo KM, Wang Y et al (2018) \u2018Mix-to-Go\u2019 silver colloidal strategy for prostate cancer molecular profiling and risk prediction. Anal Chem 90:12698\u201312705","journal-title":"Anal Chem"},{"key":"1319_CR62","doi-asserted-by":"publisher","first-page":"5249","DOI":"10.1038\/s41598-017-05489-5","volume":"7","author":"J Munkley","year":"2017","unstructured":"Munkley J, McClurg UL, Livermore KE et al (2017) The cancer-associated cell migration protein TSPAN1 is under control of androgens and its upregulation increases prostate cancer cell migration. Sci Rep 7:5249. https:\/\/doi.org\/10.1038\/s41598-017-05489-5","journal-title":"Sci Rep"},{"issue":"3","key":"1319_CR63","doi-asserted-by":"publisher","first-page":"297","DOI":"10.7150\/jca.12771","volume":"7","author":"M Albitar","year":"2016","unstructured":"Albitar M, Ma W, Lund L et al (2016) Predicting prostate biopsy results using a panel of plasma and urine biomarkers combined in a scoring system. J Cancer 7(3):297\u2013303","journal-title":"J Cancer"},{"key":"1319_CR64","doi-asserted-by":"publisher","DOI":"10.1038\/s41419-019-1830-8","author":"R Willbold","year":"2019","unstructured":"Willbold R, Wirth K, Martini T, Holger S, Wittig R (2019) Excess hepsinproteolytic activity limits oncogenic signaling and induces ER stress and autophagy in prostate cancer cells. Cell Death Dis. https:\/\/doi.org\/10.1038\/s41419-019-1830-8","journal-title":"Cell Death Dis"},{"issue":"8","key":"1319_CR65","doi-asserted-by":"publisher","first-page":"e0132743","DOI":"10.1371\/journal.pone.0132743","volume":"10","author":"Y Qi","year":"2015","unstructured":"Qi Y, Li Y, Zhang Y, Zhang L, Wang Z (2015) IFI6 inhibits apoptosis via mitochondrial-dependent pathway in dengue virus 2 infected vascular endothelial cells. PLoS ONE 10(8):e0132743","journal-title":"PLoS ONE"},{"key":"1319_CR66","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1038\/s41564-018-0244-1","volume":"3","author":"RR Blake","year":"2018","unstructured":"Blake RR, Ohlson MB, Eitson JL et al (2018) A CRISPR screen identifies IFI6 as an ER-resident interferon effector that blocks flavivirus replication. Nat Microbiol 3:1214\u20131223","journal-title":"Nat Microbiol"},{"issue":"5","key":"1319_CR67","first-page":"337","volume":"24","author":"YY Choi","year":"2008","unstructured":"Choi YY, Cho HD, Park DG, Kim SY, Baek MJ (2008) Expression of hypoxia-inducible factor-1\u03b1 and vascular endothelial growth factor in colon cancer: relationship to the prognosis and tumor markers. Ann Coloproctol 24(5):337","journal-title":"Ann Coloproctol"},{"issue":"22","key":"1319_CR68","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.11569\/wcjd.v18.i22.2379","volume":"18","author":"HJ Mia","year":"2010","unstructured":"Mia HJ, Qi XG (2010) Role of cxcl8\/cxcr1 in the metastasis of human colon cancer. World Chin J Digestol 18(22):2379","journal-title":"World Chin J Digestol"},{"key":"1319_CR69","doi-asserted-by":"publisher","first-page":"4902","DOI":"10.1002\/jcp.29368","volume":"235","author":"Q Zhao","year":"2020","unstructured":"Zhao Q, Jiang C, Gao Q, Zhang Y, Wang G, Chen X et al (2020) Gene expression and methylation profiles identified cxcl3 and cxcl8 as key genes for diagnosis and prognosis of colon adenocarcinoma. J Cell Physiol 235:4902\u20134912","journal-title":"J Cell Physiol"},{"issue":"23","key":"1319_CR70","first-page":"5495","volume":"58","author":"A Garrido","year":"1998","unstructured":"Garrido A, Fromentin A, Bonnotte B, Favre N, Moutet M, Arrigo AP et al (1998) Heat shock protein 27 enhances the tumorigenicity of immunogenic rat colon carcinoma cell clones. Can Res 58(23):5495\u20135949","journal-title":"Can Res"},{"issue":"5","key":"1319_CR71","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.3892\/or_00000125","volume":"20","author":"Tsuruta,","year":"2008","unstructured":"Tsuruta, (2008) Heat shock protein 27, a novel regulator of 5-fluorouracil resistance in colon cancer. Oncol Rep 20(5):1165\u20131172. https:\/\/doi.org\/10.3892\/or_00000125","journal-title":"Oncol Rep"},{"issue":"4","key":"1319_CR72","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1097\/00000658-198110000-00010","volume":"194","author":"PK Donahoe","year":"1981","unstructured":"Donahoe PK, Fuller AF, Scully RE, Guy SR, Budzik GP (1981) Mullerian inhibiting substance inhibits growth of a human ovarian cancer in nude mice. Ann Surg 194(4):472\u2013480","journal-title":"Ann Surg"},{"issue":"10","key":"1319_CR73","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1093\/jnen\/nlz079","volume":"78","author":"S Masahiro","year":"2019","unstructured":"Masahiro S, Hideomi H, Hiroyuki H, Suzuki SO, Masaki T, Tetsuro A et al (2019) Upregulation of Annexin A1 in reactive astrocytes and its subtle induction in microglia at the boundaries of human brain infarcts. J NeuropatholExpNeurol 78(10):961\u2013970. https:\/\/doi.org\/10.1093\/jnen\/nlz079","journal-title":"J NeuropatholExpNeurol"},{"key":"1319_CR74","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1111\/cns.13152","volume":"26","author":"YF Gao","year":"2020","unstructured":"Gao YF, Liu JY, Mao XY et al (2020) LncRNA FOXD1-AS1 acts as a potential oncogenic biomarker in glioma. CNS NeurosciTher 26:66\u201375. https:\/\/doi.org\/10.1111\/cns.13152","journal-title":"CNS NeurosciTher"},{"issue":"2","key":"1319_CR75","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1006\/bbrc.1993.1881","volume":"194","author":"K Kitamura","year":"1993","unstructured":"Kitamura K, Sakata J, Kangawa K, Kojima M, Matsuo H, Eto T (1993) Cloning and characterization of cDNA encoding a precursor for human adrenomedullin. BiochemBiophys Res Commun 194(2):720\u2013725","journal-title":"BiochemBiophys Res Commun"},{"issue":"1","key":"1319_CR76","doi-asserted-by":"publisher","first-page":"12866","DOI":"10.1038\/s41598-018-31172-4","volume":"8","author":"R Rodrigues-Pinto","year":"2018","unstructured":"Rodrigues-Pinto R, Ward L, Humphreys M et al (2018) Human notochordal cell transcriptome unveils potential regulators of cell function in the developing intervertebral disc. Sci Rep 8(1):12866. https:\/\/doi.org\/10.1038\/s41598-018-31172-4","journal-title":"Sci Rep"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01319-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01319-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01319-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T20:13:33Z","timestamp":1724876013000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01319-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,20]]},"references-count":76,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["1319"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01319-3","relation":{"references":[{"id-type":"uri","id":"","asserted-by":"subject"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,20]]},"assertion":[{"value":"26 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}